In this manuscript, we study the statistical properties of convex clustering. We establish that
convex clustering is closely related to single linkage hierarchical clustering and k-means …

Clustering is a fundamental problem in unsupervised learning. Popular methods like K-
means, may suffer from poor performance as they are prone to get stuck in its local minima …

Cluster analysis is a fundamental tool for pattern discovery of complex heterogeneous data.
Prevalent clustering methods mainly focus on vector or matrix-variate data and are not …

We study the nonparametric maximum likelihood estimator (NPMLE) for estimating
Gaussian location mixture densities in d-dimensions from independent observations. Unlike …

Convex clustering, a convex relaxation of k-means clustering and hierarchical clustering,
has drawn recent attentions since it nicely addresses the instability issue of traditional …

This paper introduces estimation methods for grouped latent heterogeneity in panel data
quantile regression. We assume that the observed individuals come from a heterogeneous …

Traditional partition-based clustering is very sensitive to the initialized centroids, which are
easily stuck in the local minimum due to their nonconvex objectives. To this end, convex …

Standard clustering methods such as K-means, Gaussian mixture models, and hierarchical
clustering are beset by local minima, which are sometimes drastically suboptimal. Moreover …

In mixed multi-view data, multiple sets of diverse features are measured on the same set of
samples. By integrating all available data sources, we seek to discover common group …

We develop a novel data-driven nonlinear mixup mechanism for graph data augmentation
and present different mixup functions for sample pairs and their labels. Mixup is a data …